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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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pipeline_tag: image-classification |
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tags: |
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- face_recognition |
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- svm |
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- facenet |
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- computer_vision |
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- streamlit |
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- cpu_friendly |
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datasets: |
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- AI-Solutions-KK/face_recognition_demo_dataset |
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--- |
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# 🧠 Face Recognition Model (CNN Embeddings + SVM) |
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**Domain-specific face recognition model** using: |
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- **FaceNet (InceptionResnetV1)** to extract 512-D face embeddings |
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- **SVM classifier** for identity recognition |
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- **Centroid baseline** for cosine-similarity checks / open-set support |
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Designed to run efficiently on **CPU**, ideal for lightweight deployment and Streamlit apps. |
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--- |
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## 📦 Artifacts in This Repository |
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| File | Description | |
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|-------------------|---------------------------------------------------------| |
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| `svc_model.pkl` | Trained SVM classifier on FaceNet embeddings (105 classes) | |
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| `centroids.npy` | Class centroids (mean embeddings per identity) | |
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| `classes.npy` | List of identity labels (class order used by the SVM) | |
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| `README.md` | Model documentation | |
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--- |
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## 🚀 Load Model from Hugging Face |
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```python |
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from huggingface_hub import hf_hub_download |
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import joblib |
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import numpy as np |
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REPO_ID = "AI-Solutions-KK/face_recognition" |
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svc_path = hf_hub_download(REPO_ID, "svc_model.pkl") |
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centroids_path = hf_hub_download(REPO_ID, "centroids.npy") |
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classes_path = hf_hub_download(REPO_ID, "classes.npy") |
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svc_model = joblib.load(svc_path) |
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centroids = np.load(centroids_path) |
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class_names = np.load(classes_path, allow_pickle=True) |
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print("Model loaded successfully. Classes:", len(class_names)) |
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``` |
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--- |
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## 🔮 Simple Inference Example (Using FaceNet Embeddings) |
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```python |
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from huggingface_hub import hf_hub_download |
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import joblib, numpy as np, cv2, torch |
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from facenet_pytorch import InceptionResnetV1, MTCNN |
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REPO_ID = "AI-Solutions-KK/face_recognition" |
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# Load classifier + metadata |
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svc_path = hf_hub_download(REPO_ID, "svc_model.pkl") |
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classes_path = hf_hub_download(REPO_ID, "classes.npy") |
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obj = joblib.load(svc_path) |
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svc_model = obj["clf"] |
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normalizer = obj["norm"] |
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label_encoder = obj["le"] |
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class_names = np.load(classes_path, allow_pickle=True) |
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# Load FaceNet backbone + face detector |
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device = "cpu" |
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mtcnn = MTCNN(keep_all=False, device=device) |
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facenet = InceptionResnetV1(pretrained="vggface2").eval().to(device) |
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def get_embedding(img_path: str) -> np.ndarray: |
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img_bgr = cv2.imread(img_path) |
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if img_bgr is None: |
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raise ValueError(f"Could not read image: {img_path}") |
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img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB) |
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face = mtcnn(img_rgb) |
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if face is None: |
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raise ValueError("No face detected.") |
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if face.dim() == 3: |
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face = face.unsqueeze(0) |
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with torch.no_grad(): |
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emb = facenet(face.to(device)).cpu().numpy() |
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return emb |
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def predict_face(img_path: str): |
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emb = get_embedding(img_path) |
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emb_norm = normalizer.transform(emb) |
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probs = svc_model.predict_proba(emb_norm)[0] |
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idx = np.argmax(probs) |
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label = label_encoder.inverse_transform([idx])[0] |
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confidence = float(probs[idx]) |
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return label, confidence |
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# -------- RUN ---------- |
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img_path = "test.jpg" |
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label, prob = predict_face(img_path) |
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print("Predicted Identity:", label) |
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print("Confidence Score:", prob) |
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``` |
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--- |
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## ⚠️ Important: Domain-Specific / Closed-Set Model |
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- This SVM is trained on **105 specific identities** from the dataset |
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`AI-Solutions-KK/face_recognition_dataset`. |
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- It will **always** predict one of these 105 classes, even for unseen people. |
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- For **new datasets / new identities**, you must retrain: |
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1. Compute new embeddings |
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2. Train SVM |
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3. Save: `svc_model.pkl`, `classes.npy`, `centroids.npy` |
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--- |
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## 🔗 Related Repositories & Live Demo |
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- **Dataset Repo** |
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https://huggingface.co/datasets/AI-Solutions-KK/face_recognition_dataset |
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- **Demo App (Hugging Face)** |
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https://huggingface.co/spaces/AI-Solutions-KK/face_recognition_model_demo_app |
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- **Stable Public Streamlit App** |
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https://facerecognition-tq32v5qkt4ltslejzwymw8.streamlit.app/ |
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- **Full Training Code & Documentation** |
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https://github.com/AI-Solutions-KK/face_recognition_cnn_svm |
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--- |
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## 🧑🔧 Train on Your Own Dataset |
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1. Prepare dataset (`root/class_name/image.jpg`) |
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2. Extract embeddings (FaceNet or your own) |
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3. Train SVM or cosine classifier |
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4. Save: |
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- `svc_model.pkl` |
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- `classes.npy` |
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- `centroids.npy` |
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Then plug into your own app or the provided Streamlit demo. |
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--- |
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## 👤 Author |
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**Karan (AI-Solutions-KK)** |
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